A Partial Least Squares‐Based Consensus Regression Method for the Analysis of Near‐Infrared Complex Spectral Data of Plant Samples

Abstract

A consensus regression approach based on partial least square (PLS) regression, named as cPLS, for calibrating the NIR data was investigated. In this approach, multiple independent PLS models were developed and integrated into a single consensus model. The utility and merits of the cPLS method were demonstrated by comparing its results with those from a regular PLS method in predicting moisture, oil, protein, and starch contents of corn samples using the NIR spectral data. It was found that cPLS was superior to regular PLS with respect to prediction accuracy and robustness.

Keywords:

• Near‐infrared spectroscopy
• partial least squares
• consensus modeling
• multivariate calibration

Acknowledgments

The authors gratefully acknowledge the National Center for Toxicological Research (NCTR) of the U.S. Food and Drug Administration (FDA) and SAS Inc. for postdoctoral support through the Oak Ridge Institute for Science and Education.